Preadaptation and spatial effects on retention-tree survival in cut areas in Estonia
Bibliographic record
Abstract
Survival of live retention trees is a key issue for stand-scale applications of natural-disturbance-based silviculture. We explored the survival of 3255 trees in 102 cut areas (mean size 2.3 ha) in Estonia for 6 years, focusing on spatial variation and preadaptation of the trees. Altogether, 35% of the trees died during the study period, contributing 4.4 m3 of downed dead trunks and 1 m3 of standing dead trees per hectare. The annual mortality rates declined over time. The main survival determinants were tree species (higher for hardwood deciduous trees), diameter (species dependent), position relative to forest edge (higher for trees near current or former forest edges), retention density (positive), and exposure (negative). The results suggest that (1) green-tree retention can effectively increase the abundance of large shade-tolerant trees, but it is equally important for producing deadwood; (2) larger individuals, former interior-forest trees near current forest edges, and preadapted trees in open conditions should be preferably retained; (3) there is no obvious necessity to modify tree-retention techniques for tree survival according to geographical region or forest site type.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".